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+---
+pipeline_tag: sentence-similarity
+license: apache-2.0
+tags:
+- text2vec
+- feature-extraction
+- sentence-similarity
+- transformers
+---
+# shibing624/text2vec
+This is a CoSENT(Cosine Sentence) model: It maps sentences to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
+## Usage (text2vec)
+Using this model becomes easy when you have [text2vec](https://github.com/shibing624/text2vec) installed:
+```
+pip install -U text2vec
+```
+Then you can use the model like this:
+```python
+from text2vec import SBert
+sentences = ['如何更换花呗绑定银行卡', '花呗更改绑定银行卡']
+
+model = SBert('shibing624/text2vec-base-chinese')
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+## Usage (HuggingFace Transformers)
+Without [text2vec](https://github.com/shibing624/text2vec), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
+```python
+from transformers import BertTokenizer, BertModel
+import torch
+
+# Mean Pooling - Take attention mask into account for correct averaging
+def mean_pooling(model_output, attention_mask):
+    token_embeddings = model_output[0]  # First element of model_output contains all token embeddings
+    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+
+# Load model from HuggingFace Hub
+tokenizer = BertTokenizer.from_pretrained('shibing624/text2vec-base-chinese')
+model = BertModel.from_pretrained('shibing624/text2vec-base-chinese')
+sentences = ['如何更换花呗绑定银行卡', '花呗更改绑定银行卡']
+# Tokenize sentences
+encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
+
+# Compute token embeddings
+with torch.no_grad():
+    model_output = model(**encoded_input)
+# Perform pooling. In this case, max pooling.
+sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
+print("Sentence embeddings:")
+print(sentence_embeddings)
+```
+## Evaluation Results
+For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [text2vec](https://github.com/shibing624/text2vec)
+
+## Full Model Architecture
+```
+SBert(
+  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_mean_tokens': True})
+)
+```
+## Citing & Authors
+This model was trained by [text2vec/cosent](https://github.com/shibing624/text2vec/cosent). 
+        
+If you find this model helpful, feel free to cite:
+```bibtex 
+@software{text2vec,
+  author = {Xu Ming},
+  title = {text2vec: A Tool for Text to Vector},
+  year = {2022},
+  url = {https://github.com/shibing624/text2vec},
+}
+```